graph reasoning

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papersTODAY 04:00 UTC

arXiv paper proposes skill-augmented graph reasoning for table question answering

A revised arXiv preprint introduces a method for table question answering that treats questions differently instead of uniformly, pairing learned skills with graph-based reasoning over table structures. The authors argue that reporting only overall accuracy hides a sharp divide between easy lookup questions and harder multi-step operations. The approach, called skill-augmented table graph reasoning, targets operation-wise evaluation of large language models on tabular data.

table-qa/reasoning/llm-evaluation/arxiv

papersSEP 10 04:00 UTC

Multi-Agent Agentic Graph Learning via Structural Signatures

A newly posted arXiv paper proposes a multi-agent extension of agentic graph learning, a technique where LLM-driven agents sample parts of a graph as evidence before making a prediction. The approach uses structural signatures to coordinate how multiple agents divide and process graph reasoning tasks. The work targets improved performance on graph reasoning benchmarks compared with prior single-agent methods.

agents/graph-learning/llm/research